(a) Field of the Invention
The present invention relates to an automated mammographic density estimation and display method, an automated mammographic density estimation system for the same, and a media for storing a computer program for the method, and more particularly to a breast area segmentation and automated mammographic density estimation and display method based on prior probability information, an automated mammographic density estimation system for the same, and a media for storing a computer program for the method, in which learned knowledge of expert readers is incorporated into the prior probability information and used as a basis in estimating the mammographic density, thereby allowing more reliable and accurate mammographic density estimation.
(b) Description of the Related Art
Breast cancer is one of the most significant health issues worldwide. According to the reported statistics, approximately 1 in 8 women will develop invasive breast cancer over the course of their lifetime, and breast cancer is the second leading cause of the mortality among women in Unite States.
Prognosis factors of the breast cancer includes physiologically important factors such as histological differentiation, axillary lymph node metastasis, a tumor size, age at onset, whether estrogen and progesterone receptors are positive or not, whether c-erbB2 receptors are positive or not, and so on. However, with recent development of mammography, a mammogram has been widely used as an early screening test before checking up such factors.
In North America and Europe, screening mammography has been carried out under the national support since the 1960s, and it has been reported that a death rate from the breast cancer was reduced as a result from trying to early detect the breast cancer. Through the mammogram, a mass or a microcalcification is generally detected to estimate a risk of the breast cancer. However, overall breast density in the mammography has also recently been used a lot for canner screening.
Meanwhile, Wolfe, et al. has asserted that the mammographic density correlates with the risk of the caner occurrence, and many studies have reported that higher mammographic density is an independent risk factor for the bread caner occurrence even though it reduces a breast cancer detection rate in the mammography.
The mammographic density refers to a proportion of glandular tissues within the breast area as depicted by mammogram, in which a qualitative estimation method is typically used to classify the mammographic density according to breast imagine reporting and data system (BI-RADS).
Thus, the mammographic density in the mammogram is widely accepted as a useful factor in the early detection for the bread cancer and is being used in such a manner that a reading doctor qualitatively determines the proportion of the glandular tissues observed in the mammogram with his/her naked eyes. However, the mammographic density as estimated by such a qualitative way is known to suffer significant variation depending on observer's experience as well as observer's physiologic condition, which leads to a high degree of inter-observer and intra-observer variabilities.
Accordingly, a computer-aided diagnosis (CAD) has been actively studied for an objective, quantitative and accurate estimation of mammographic density.
Regarding to this, the paper published in 1994 by the Byng study group in University of Toronto, as one of pioneering study groups, is based on a method where two radiologists first distinguish the breast area from the mammogram through the first thresholding and then distinguish the glandular tissues again through the second thresholding. This method is regarded as a pioneering study in the field of computer assisted estimation of the mammographic density, but has disadvantages that reproducibility is deteriorated and a great deal of care is required because the estimation is manually carried out. Also, Zhou et al. in University of Michigan employed a method that histogram characteristics of the breast area are sorted into four BI-RADS categories and corresponding thresholding is automatically implemented, thereby distinguishing the glandular tissues. Further, Saha et al. in University of Pennsylvania defined a certain region in the histogram of the breast area as a seed region and used a fuzzy connectivity method based on the seed region, thereby automatically estimating the mammographic density. Lately, Oliver et al. in University of Girona distinguished the tissues having similar characteristics through a C-means clustering method, and classified the distinguished clusters into the BI-RADS categories through a Combined Bayesian method.
The present invention discloses an automated mammographic density estimation and display method based on prior probability information, in which learned knowledge of expert readers is incorporated into the prior probability information and used as a basis in estimating the mammographic density, thereby allowing more reliable and accurate mammographic density estimation.